The present invention relates generally to language translation supply chains and, more particularly, to a translation synthesizer for analysis, amplification and remediation of linguistic data across translation supply chains.
Today, machine-human translation services are employed by firms to produce high quality and human fluent translations using an integration of linguistic assets/corpuses, computer-aided translation editors, human professional linguists and operational management systems across a translation supply chain. The art of measuring linguistic noise (errors) allows the language translation operations of a translation supply chain to measure noise at the component and supply chain levels. Lacking is the ability to measure productivity per unit across linguistic integrated components (L-ICs), and the ability to identify and quantify grammatical/syntactic/semantic (GSS) patterns that impact the efficiency of the L-ICs. Specifically, conventional translation operations are lacking in the following areas: (1) there is no uniform method for classifying/categorizing Linguistic noise (GSS) patterns at operational levels; (2) there is no open/public method for the plug-and-play configuration of a translation supply chain integrating L-ICs; (3) there is no uniform unit for measuring the productivity per unit of L-IC within a translation supply chain (specifically lacking is the ability to measure unit productivity at the segment level); (4) there is no method for quantifying a noise:word ratio as a primary measurement of productivity per segment; (5) there is no operational visualization of Linguistic noise (GSS) patterns nor the ability to remediate the increasing Linguistic noise (GSS) patterns across a global translation supply chain; and (6) there are no methods for identifying the acceptable range (i.e. thresholds) for GSS markers and the ability to map a GSS marker to a pluggable remediation handler function.